MobileSal: Extremely Efficient RGB-D Salient Object Detection

The high computational cost of neural networks has prevented recent successes in RGB-D salient object detection (SOD) from benefiting real-world applications. Hence, this paper introduces a novel network, \methodname, which focuses on efficient RGB-D SOD by using mobile networks for deep feature extraction. The problem is that mobile networks are less powerful in feature representation than cumbersome networks. To this end, we observe that the depth information of color images can strengthen the feature representation related to SOD if leveraged properly. Therefore, we propose an implicit depth restoration (IDR) technique to strengthen the feature representation capability of mobile networks for RGB-D SOD. IDR is only adopted in the training phase and is omitted during testing, so it is computationally free. Besides, we propose compact pyramid refinement (CPR) for efficient multi-level feature aggregation so that we can derive salient objects with clear boundaries. With IDR and CPR incorporated, \methodname~performs favorably against \sArt methods on seven challenging RGB-D SOD datasets with much faster speed (450fps) and fewer parameters (6.5M). The code will be released.